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N=1 experimentation methods can be used by individuals for healing and enhancing performance.

Personalized Data

Many people now have access to unprecedented amounts of data about themselves (thanks, tracking apps!) but most of us aren’t sure how to use it to improve our health.

Some people aren’t into wearing tracking gadgets, but are still interested in gathering and using n=1 data for healing.

In this post, we get back-to-basics. It’s an introduction to graphing as a simple and effective strategy for storing, interpreting and sharing your n=1 data.

Graphing for Healing

Graphing, or charting, is an effective way to depict relationships between numbers.

When you have a lot of numeric data, it isn’t always easy to see the relationships between those measures or to discern trends over time. A graph helps you to do both.

It also lets you to summarize a large amount of information in one place, which makes your data easy to store and to share.

This graph depicts some of Matthew’s data over 34 months, starting when he made a long-term commitment to a healing protocol lifestyle.

The same measures, gathered over time, tell a story.

What this graph doesn’t depict are the various n=1 experiments he was running during this time period. Adding that information would enable a more thorough analysis of cause and effect.

Data for Learning

This graph enabled Matthew to learn that his nausea was likely not autoimmune in origin.

One of his healing strategies was a long-term commitment to the elimination phase of the Autoimmune Protocol (AIP). All of his inflammation-related symptoms, including autoimmune arthritis and psoriasis, pain-medication use (for treating pain associated with these conditions), and brain fog improved on the AIP.

By the summer of 2015 it was apparent that the nausea was not following the same trajectory as his other symptoms. That was helpful information, as none of his doctors could figure out what was causing the nausea. Finally there was one clue to help solve the mystery: the nausea was probably not autoimmune. That led to new strategies that did help to reduce his nausea.

Graphing over time also let Matthew establish a seasonal baseline, as his symptoms tend to be worse in the winter and early spring. That way, he could compare his symptoms to the previous season (winter to winter) as a way of understanding the progress he was making, rather than interpreting the increase in symptoms between summer and winter as a decline in his health overall.

The basics

An introduction to n=1 graphing:

There are 2 ‘axes’ on every graph: the horizontal (the x-axis) and vertical (the y-axis).

You get to decide what each axis represents. In n=1 experiments, one usually represents time. The x-axis might represent the value of what you are tracking (pain levels from 0-7, for example). In that case, let the y-axis represent time. Often the timeframe for healing is months, but there are some measures that you might want to track weekly or daily, if you prefer.

To start, label each axis so it is clear what it represents. Add the numbers, at whatever interval is appropriate.

If you add data as you collect it, your graph will always be up to date.

Tracking multiple variables enables you to compare them to each other.

Keep it simple: You can graph with a pencil and paper, or use the ‘chart’ function in a word processing or spreadsheet program.

Keen to get started?

Need data?

Biohack U has a confidential, comprehensive health assessment that will give you numeric data to drop into your graph. Find that here.

Start today!

If you’d like a friendly reminder to measure at regular intervals, sign up for the Biohack U newsletter. You can do that when you send your assessment results to yourself or by checking the sign-up box here. Many people use these newsletters, which always include a link to the health assessment as well as tips for n=1 experimentation, as a cue to complete another assessment.

The newsletters will arrive in your inbox every two weeks.

If you use them as your reminder, you’ll have 26 data points for each measure of interest (you decide what is of interest) within a year. That will make a very respectable and useful graph.